Edith Cowan University, Research Online, Perth, Western Australia
Abstract
Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used for evolving the neural network topology until an optimal topology is reached. The model was applied on a popular open source program, namely, Android. The results are very promising, where the correlation between actual and predicted points reaches 0.9
